Keywords : iris


Iris Recognition System Based on Wavelet Transform

Maha A. Hasso; Bayez K. Al-Sulaifanie; Kaydar M. Quboa

AL-Rafidain Journal of Computer Sciences and Mathematics, 2009, Volume 6, Issue 2, Pages 105-116
DOI: 10.33899/csmj.2009.163801

In order to provide accurate recognition of individuals, the most discriminating information present in an iris pattern must be extracted. Only the significant features of the iris must be encoded so that comparisons between templates can be made. Most iris recognition systems make use of a band pass decomposition of the iris image to create a biometric template. In this paper, the feature extraction techniques are improved and implemented. These techniques are using wavelet filters. The encoded data by wavelet filters are converted to binary code to represent the biometric template. The Hamming distance is used to classify the iris templates, and the False Accept Rate (FAR), False Reject Rate (FRR)  and recognition rate (RR)  are calculated [1]. 
            The wavelet transform using DAUB12 filter proves that it is a good feature extraction technique. It gives equal FAR and FRR  and a high recognition rate for the two used databases. When applying the DAUB12 filter to CASIA database, the FAR and FRR are equal to 1.053%, while the recognition rate is 97.89%. For Bath database the recognition rate when applying DAUB12 filter is 100%. CASIA and Bath databases are obtained through personal communication. These databases are used in this paper.
 

Personal Identification with Iris Patterns

Mazin R. Khalil; Mahmood S. Majeed; Raid R. Omar

AL-Rafidain Journal of Computer Sciences and Mathematics, 2009, Volume 6, Issue 1, Pages 13-26
DOI: 10.33899/csmj.2009.163762

This research is aimed to design an iris recognition system. There are two main steps to verify the goal. First: applying image processing techniques on the picture of an eye for data acquisition. Second: applying neural networks techniques for identification.
The image processing techniques display the steps for getting a very clear iris image necessary for extracting data from the acquisition of eye image. This picture contains all the eye (iris, pupil and lashes). So, the localization of the iris is very important. The new picture should be enhanced to bring out the pattern. The enhanced picture is segmented into 100 parts, then a standard Deviation (STD) can easily be computed for every part. These values will be used in the neural network for the identification.
For neural network techniques, Backprobagation neural network was used for comparisons. The weights and output values will be stored in a text file to be used later in identification. The Backprobagation network succeeded in identification and attained to (False Acceptance Rate = 10% - False Rejection Rate = 10%).
 

Pupil Detection Execution Time Reduction in Iris Recognition System

Maha A. Hasso; Bayez K. Al-Sulaifanie; Kaydar M. Quboa

AL-Rafidain Journal of Computer Sciences and Mathematics, 2008, Volume 5, Issue 2, Pages 183-192
DOI: 10.33899/csmj.2008.163981

Iris recognition is regarded as the most reliable and accurate biometric identification system available. The work presented in this paper involves improving iris segmentation to reduce execution time. To determine the performance of the iris system two databases of digitized grayscale eye images are used.
The segmentation process in the iris recognition system is used to localize the circular iris and pupil regions, excluding eyelids and eyelashes. New techniques are proposed and implemented for pupil detection. These techniques are mask, profile and the combined profile mask (CPM) technique. The extracted iris region is normalized into a rectangular block with constant dimensions to account for imaging inconsistencies.
The feature extraction technique is based on 2D Gabor filters. The Hamming distance is used to classify the iris templates, and the FAR, FRR and RR are calculated. 
The results of the study proved that the best technique for pupil detection is when using the combined technique. It gives about 100% success rate for pupil detection.